class_docstr: Class for masking of Niimg-like objects.
  
  MultiNiftiMapsMasker is useful when data from overlapping volumes
  and from different subjects should be extracted (contrary to
  :class:`nilearn.maskers.NiftiMapsMasker`).
  
  .. note::
      Inf or NaN present in the given input images are automatically
      put to zero rather than considered as missing data.
  
  Parameters
  ----------
  maps_img : 4D niimg-like object
      See :ref:`extracting_data`.
      Set of continuous maps. One representative time course per map is
      extracted using least square regression.
  
  mask_img : 3D niimg-like object, optional
      See :ref:`extracting_data`.
      Mask to apply to regions before extracting signals.
  
  allow_overlap : :obj:`bool`, default=True
      If False, an error is raised if the maps overlaps (ie at least two
      maps have a non-zero value for the same voxel).
  %(smoothing_fwhm)s
  %(standardize_maskers)s
  %(standardize_confounds)s
  high_variance_confounds : :obj:`bool`, default=False
      If True, high variance confounds are computed on provided image with
      :func:`nilearn.image.high_variance_confounds` and default parameters
      and regressed out.
  %(detrend)s
  %(low_pass)s
  %(high_pass)s
  %(t_r)s
  dtype : {dtype, "auto"}, optional
      Data type toward which the data should be converted. If "auto", the
      data will be converted to int32 if dtype is discrete and float32 if it
      is continuous.
  
  resampling_target : {"data", "mask", "maps", None}, default="data"
      Gives which image gives the final shape/size:
  
          - "data" means the atlas is resampled to the shape of the data if
            needed
          - "mask" means the maps_img and images provided to fit() are
            resampled to the shape and affine of mask_img
          - "maps" means the mask_img and images provided to fit() are
            resampled to the shape and affine of maps_img
          - None means no resampling: if shapes and affines do not match,
            a ValueError is raised.
  
  
  %(memory)s
  %(memory_level)s
  %(n_jobs)s
  %(verbose0)s
  reports : :obj:`bool`, default=True
      If set to True, data is saved in order to produce a report.
  %(masker_kwargs)s
  
  Attributes
  ----------
  maps_img_ : :obj:`nibabel.nifti1.Nifti1Image`
      The maps mask of the data.
  
  n_elements_ : :obj:`int`
      The number of overlapping maps in the mask.
      This is equivalent to the number of volumes in the mask image.
  
      .. versionadded:: 0.9.2
  
  Notes
  -----
  If resampling_target is set to "maps", every 3D image processed by
  transform() will be resampled to the shape of maps_img. It may lead to a
  very large memory consumption if the voxel number in maps_img is large.
  
  See Also
  --------
  nilearn.maskers.NiftiMasker
  nilearn.maskers.NiftiLabelsMasker
  nilearn.maskers.NiftiMapsMasker
docstr: Apply mask, spatial and temporal preprocessing.
  
  Parameters
  ----------
  %(imgs)s
      Images to process. Each element of the list is a 4D image.
  %(confounds)s
  %(sample_mask)s
  
  Returns
  -------
  region_signals : list of 2D :obj:`numpy.ndarray`
      List of signals for each map per subject.
      shape: list of (number of scans, number of maps)
docstr_tok: ["Apply","mask",",","spatial","and","temporal","preprocessing",".","Parameters","--","--","--","--","--","%","(","imgs",")","s","Images","to","process",".","Each","element","of","the","list","is","a","4D","image",".","%","(","confounds",")","s","%","(","sample_mask",")","s","Returns","--","--","--","-","region_signals",":","list","of","2D",":","obj",":","`","numpy.ndarray","`","List","of","signals","for","each","map","per","subject",".","shape",":","list","of","(","number","of","scans",",","number","of","maps",")"]
code: def transform(self, imgs, confounds=None, sample_mask=None):
      """Apply mask, spatial and temporal preprocessing.
  
      Parameters
      ----------
      %(imgs)s
          Images to process. Each element of the list is a 4D image.
      %(confounds)s
      %(sample_mask)s
  
      Returns
      -------
      region_signals : list of 2D :obj:`numpy.ndarray`
          List of signals for each map per subject.
          shape: list of (number of scans, number of maps)
  
      """
      self._check_fitted()
      if not hasattr(imgs, "__iter__") or isinstance(imgs, str):
          return self.transform_single_imgs(imgs)
      return self.transform_imgs(
          imgs, confounds, n_jobs=self.n_jobs, sample_mask=sample_mask
      )
  
file_path: nilearn/nilearn/maskers/multi_nifti_maps_masker.py
imports: import itertools
  from joblib import Memory, Parallel, delayed
  from .._utils import fill_doc
  from .._utils.niimg_conversions import iter_check_niimg
  from .nifti_maps_masker import NiftiMapsMasker
label_desc: Use image node_id 3 for calling the MultiNiftiMapsMasker obj's underlying member method code with example usage: obj.transform(imgs, confounds, sample_mask) and returns: self, self